Un-calibrated HVAC sensors can cause a substantial increase in user discomfort and energy consumption (e.g., approximately 2%-4%). A conventional HVAC sensor network is typically calibrated manually as a part of an annual maintenance regime. Thus, conventional sensor calibration methods introduce high commissioning efforts and maintain poor sensor accuracy. Other conventional HVAC sensor calibration methods include fusing data from different sensors to estimate the real sensor reading such that the correlation between sensor readings is evaluated using learned building models. These models, however, require parameter tuning using trustable data sets. In this case, creating the models and obtaining the trusted data are the main challenges and require high commissioning cost.